class_docstr: Unit normalization layer.
  
  Normalize a batch of inputs so that each input in the batch has a L2 norm
  equal to 1 (across the axes specified in `axis`).
  
  Example:
  
  >>> data = np.arange(6).reshape(2, 3)
  >>> normalized_data = keras.layers.UnitNormalization()(data)
  >>> print(np.sum(normalized_data[0, :] ** 2)
  1.0
  
  Args:
      axis: Integer or list/tuple. The axis or axes to normalize across.
          Typically, this is the features axis or axes. The left-out axes are
          typically the batch axis or axes. `-1` is the last dimension
          in the input. Defaults to `-1`.
code: def call(self, inputs):
      x = ops.cast(inputs, self.compute_dtype)
  
      square_sum = ops.sum(ops.square(x), axis=self.axis, keepdims=True)
      x_inv_norm = ops.rsqrt(ops.maximum(square_sum, 1e-12))
      return ops.multiply(x, x_inv_norm)
  
file_path: keras/keras/layers/normalization/unit_normalization.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
label_desc: Use image node_id 3 for calling the UnitNormalization obj's underlying member method code with example usage: obj.call(inputs) and returns: ops